The Impact of El Nino and La Nina on the United Arab Emirates (UAE) Rainfall

General Scientific Researches, Vol(4), No (1), March, 2016. pp. 5-10

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The Impact of El Niño and La Niña on the United Arab Emirates (UAE) Rainfall Mohamed AlEbri* Department of Meteorology, National Centre of Meteorology & Seismology (NCMS), Abu Dhabi, UAE.

Hasan Arman Professor. Department of Environmental Science, College of Science, UAE University (UAEU), AlAin, UAE.

Abdeltawab Shalaby Department of Research & Development, National Centre of Meteorology & Seismology (NCMS), Abu Dhabi, UAE. *Corresponding author: [email protected]

Keywords

Abstract

El Niño and La Niña Rainfall Drought Index (EDI) UAE

This study focuses on the impact of El Niño and La Niña on rainfall in the United Arab Emirates (UAE). From the study and analysis of rainfall data for the UAE, collected between 1980 and 2013, it was found that the phenomena of El Niño and La Niña have an impact on the amount of rainfall in the United Arab Emirates. An UAE Effective Drought Index (EDI) was created from monthly rainfall data for UAE. When this index was compared with the Oceanic Nino Index (ONI), Nino 3.4 Index of the El Niño and La Niña phenomena, it could be obviously seen in the generated graph that most periods in which an El Niño was dominant there were increased amounts of rain, and vice versa for La Niña that led to less rainfall amounts. This is due to the effect of El Niño and La Niña on weather patterns in the atmosphere, such as Jet Streams and Rossby Waves that travel around the globe, UAE is one of them.

1.

Introduction

The oceanic current that alternates along the west coast of Southern America creates El Niño and La Niña patterns. These terms derive from Spanish and translate to The Boy and The Girl respectively. As winds blow from high pressure belts to low pressure belts across the Pacific Ocean, ocean currents are created following the pattern of the winds. El Niño and La Niña are anomalies to the normal pattern and are referred to as the reversed oceanic currents. In these conditions, the winds must reverse. Furthermore, El Niño and La Niña refers to the fluctuations of the sea-level pressures in the southern Pacific and the sea surface temperatures in eastern Equatorial Pacific in a time period of between two to seven years [1]. After two to seven years, warm winds move southwards along the coast of South America, and continue for as long as seven months. This is then followed by heavy rains, especially along the coastal regions of Ecuador and north Peru. These periodic warming’s are called an El Niño. In the 1960’s, scientists started to associate these abnormal periodic warm waters in South America with those throughout the equatorial pacific [2]. Furthermore, more than average warm waters were related to the oscillations of the atmospheric pressures, known as the El Niño Southern Oscillation (ENSO). In different countries, different names are used to refer to El Niño, including “El Niño Southern Oscillation episode” and “Pacific warm episode” [3]. The main difference between El Niño and La Niña is that La Niña is associated with cool waters in mostly the central equatorial pacific, while El Nino is associated with a warm water episode. This causes a change in the distribution and intensity of rainfall in the tropics and in sea level pressure patterns (this is also the high-index phase of Southern Oscillation) and it is these weather patterns in the atmosphere that affect most parts in the world. La Niña is also referred to by different names across the different regions in the world, for instance it is also known as “The Pacific Cold Episode” [4]. Rainfall is more predominant in the atmosphere above warm waters, which is why the east Pacific is therefore drier under the normal cold current. The cool waters are considered to be within fifty meters from the surface of the sea [5]. At the onset of El Niño, the trade winds cool down in the western and the central Pacific, leading to a depression of the thermocline in the eastern Pacific and its elevation in the west. This then reduces upwelling which normally cools the surface and consequently reduces the supply of nutrient rich water to the euphotic regions. As a result, a rise in the level of temperature is experienced. Scientists have been able to reach the stage where they can make rough estimates of their occurrences. Not all of these phenomena are the same, and the reaction of the atmosphere is not always the same, or rather the same atmospheric conditions are not always present each time an El Niño or La Niña is experienced [6]. The objective of this paper is to investigate the impact of La Niña and El Niño on the UAE rainfall, with intensive focus on the rainfall fluctuations of the UAE. Since this the first study on the relationship between ENSO and UAE rainfall, in this study, several procedures were used, including the study of El Niño and La Niña and the impact it has on the global scale; the investigation of the climate, with respect to the fluctuation of the rainfall over the UAE; an exploration of the suitable drought indices for UAE rainfall; and finally, a study of the correlation between the phenomena that have been mentioned above and the consequences on the climate over the UAE with respect to rainfall. More specifically, this research focuses on the UAE through conducting analysis on the historical data (rainfall) for at least the past thirty-three years. It is very important that this will lead to the exploration of the relationships between El Niño and La Niña and the rainfall in UAE.

2.

El Niño and La Niña

El Niño and La Niña are natural phenomena of the climatic system. They originate from the names of periodic development of warm waters over the oceans originally over the tropical South American oceans and at the Equator. Today, they describe the whole El Niño Southern Oscillation (ENSO), which is the climatic fluctuation occurring when the oceans warm up. La Niña thus refers to the opposite extremes of the ENSO phenomenon.

Mohamed AlEbri *, Hasan Arman, Abdeltawab Shalaby

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General Scientific Researches Vol(4), No (1), March, 2016.

La Niña is a phenomenon that is usually experienced in the oceans, and refers to the irregular cooling of sea surface temperatures, from the coasts of Latin America to the central Pacific. El Niño refers to the disruption of the oceanic atmosphere relationship along the tropical Pacific. These phenomena also influence jet stream locations and can affect the weather patterns around the world. Some of the consequences that are brought about by these two phenomena are increased rainfall amounts and severe drought across the different regions that they influence [7]. El Niño and La Niña are the key reasons of variability in climate and weather for different regions across the world. La Niña and El Niño have a cyclic alternation, called the ENSO cycle. When El Niño gives way to La Niña, this transition is often very fast compared to the reverse transition [8]. The episodes of El Niño occur during the spring season on the northern hemisphere every three to five years, and lasts between nine and twelve months. In contrast, La Niña usually lasts between one and three years, but there is variability in the duration as well as their development and intensity [9]. When both of these two are not experienced, this period is referred to as ENSO neutral. The indications of the formation of an El Niño are when warm waters build along the equator in the eastern Pacific. The surface of the sea warms the atmosphere directly above it, allowing moisture to rise and develop into precipitation. The warm waters are usually about five to seven degrees above average. La Niña, on the other hand, is created by the cool water’s surface in the equatorial eastern Pacific. La Niña impacts are usually directly opposite to the impacts of El Niño. The atmosphere directly above the cool water surface cools down, and thus less evaporation of water is experienced. The cool and dry air is dense, thus does not rise to form rainfall and storms. The resulting effects do not form as much rainfall over the pacific. This then results in dry conditions experienced in the south eastern parts of the United States and Latin America. La Niña and El Niño show the extreme ends of a cycle in the Pacific. This cycle is not so complicated, but analysis of the time series shows that the cycle oscillates with an interval of three to seven years. La Niña follows El Niño almost immediately. El Niño is developed by the trade winds, particularly when those winds are weaker than normal, whereas La Niña occurs when these winds are stronger than normal and are at the peak in the December months. Additionally, El Niño and La Niña or ENSO is referred to changing temperatures of the ocean surfaces in the tropical Pacific regions leads to rise a huge amount of water vapour in the atmosphere and that affect weather patterns aloft such as the jet streams and the Rossby waves. A jet stream refers to a current of rapidly moving air, usually some thousand metres wide and long; however, it is relatively thin. Jet streams certainly get affected hugely by the formation of El Niño and La Niña [10]. The Rossby Waves are large scale winds of jet streams that circulate the globe in series of waves. These weather patterns changes certainly have a lot of effect on UAE as they move from west to east toward the Arabian Peninsula, especially in terms of rain. When El Niño occurs, it results in increased precipitation. This is due to the polar jet streams that moved further south causing increased storms. Generally, the precipitations increase in the area where the jet streams are stronger. To date, the occurrence of the jet streams has been used to indicate the possibility of the weather activities. 3.1 Oceanic Niño Index (ONI) Meteorologists and scientists use the ONI to measure the departure of the conditions from the normal sea surface temperatures. This is a standard method of measuring El Niño episodes, forecasting, and estimating the weather. The occurrence of El Niño can be expected by observing the increase in sea surface temperatures by more than 0.5 oC, for some three or more months. The Oceanic Niño Index (ONI) simply refers to the departure from the mean effects or the observed anomaly. In analyzing the ONI, different 30-year periods are used as the basis and are then used to calculate anomalies; normally the base period is updated every five years. However, in real-time computation of these statistics, the base period will be applied to find the real departure from the mean. Additionally, the Climate Prediction Centre (CPC) generates the base year, still using the 30 years lapse, after every five years. After the five-year period of updating the ONI, the ONI value changes slightly due to the inclusion of the recently collected data. This is of advantage because the classification of La Niña and El Niño will remain more or less fixed over the historical period. Additionally, the adjustments that may happen in the future may not affect the past classification of episodes. The improvement of ONI is essential regarding its extensive use in various research and weather functions. Interpreting ONI values should be cautiously done as its development was originally for research purposes. The negative figures of ONI indicate a cold phase, which shows the possibilities of an occurrence of La Niña; the positive ONI figures indicate the presence of a warm phase, representing El Niño. One of the most important things to understand is how to identify the phase of the ENSO. The National Oceanic and Atmospheric Administration (NOAA), Climate Prediction Centre also uses the average thirty year sea surface temperature for the tropical Pacific. The differences in the observations from the average can be used to indicate the phase of ENSO. A monthly calculation is helpful to obtain the monthly variability; this is averaged with the previous monthly values and compared to the average SST. The Oceanic Niño Index is a tool used by NOAA for identifying El Niño and La Niña events. The Pacific Ocean has been divided into four regions; refer to Figure 1. These regions are Niño 1+2, Niño 3, Niño 3.4, and Niño 4. The Niño 3.4 index is around 0.5 and above. This means that if El Niño conditions exist, that the SST in the Niño 3.4 region is more than 0.5 degrees Celsius warmer than average. When it is less than 0.5, then La Niña effects have occurred, and indicates then that the SST is at least cooler than average. If the reading is between 0.5 and -0.5, then the ENSO is considered neutral [11]. As an example, the episodes of El Niño of 1982-1983 and 1997-1998 are the most intense recorded in the twentieth century; particularly the latter one which lasted for about a year. During these periods the sea temperatures in the equatorial regions and eastern tropical pacific were 5 to 10 °C above average. The ONI was the highest recorded and the surface temperatures and the weather patterns were similar. Severe droughts hit Australia, whereas Tahiti experienced severe typhoons and Chile suffered a long period of rains and increased flooding. The 1997-1998, resulted in increased drought in Brazil, Malaysia, and Indonesia, while Peru received increased rainfalls along the coast. [12]

3.

Effective Drought Index (EDI)

Effective Drought Index (EDI) is a tool used for assessing drought. It is the summation of the value of precipitation taken on a daily or monthly basis, with respect to time. It is the function of precipitation of the previous and the current days. The period of calculation of the EDI may vary, but it is normally set to 365 days for simplicity. The main idea behind discussing the objectives of EDI is to enable monitoring the possible excess and shortage of rainfall during the warm and cold seasons, respectively. In computing the EDI, the following formula was used: =∑ = =

[(∑

)⁄ ]

− /

(1) (2)

(

)

(3)

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The Impact of El Nino and La Nina on the United Arab Emirates (UAE) Rainfall General Scientific Researches Vol(4), No (1), March, 2016.

Where EPi (Effective Precipitation) represents the suitable accumulations of precipitation, Pm is the daily precipitation level representing the days preceding a particular date, and ‘i’ in equation (1) begins from 365; making EP become the valid accumulation of precipitation, 365 days from a specific date. Equation (2) in the formula is a representation of the departure of EP from MEP (Mean Effective Precipitation). The SD is the standard deviation. If DEP (Deviation Effective Precipitation) is found to be negative for two successive days, “i” becomes 366 (= 365 + 2 – 1) and the process of calculation starts again [13]. This implies that the drying consequence on soil during a drought that may have occurred years ago is shown in the EDI [14]. The UAE monthly rainfall data from 1980 - 2013 was ingested by an EDI software application and provided thirty-two years of indices from 1981 -2013. The reason for missing the first year is, as afore-mentioned, the EDI takes into account the accumulated precipitation; that is 365 days (one previous year). For both climatological and meteorological calculation of drought index (DI), the following summarizes the ranges that define the extent of wetness and dryness: Extreme drought < -2.0 < Severe drought < -1.5 < moderate drought < -1.0 =< Near normal - < 0 Extreme wet > 2.0 > Severe wet > 1.5 > moderate wet > 1.0 >= near normal + >0 EDI = 0 Normal. [15]

4.

Data Analysis and Result

Drought indices are used most frequently in determining the frequency of meteorological drought. This measurement is very simple and easy to use. EDI (Effective Drought Index) is considered a standardized index used to assess the drought severity. The daily or monthly time measurements make the EDI unlike most of the other indices, as the measurement uses precipitation as the only data variable and excludes other meteorological statistics. This makes it better for application purposes due to the availability of such data, and for extensive periods, which is especially suitable for the UAE. EDI can be calculated using a range of -2 to 2, with a threshold that can be used to measure wetness, from extremely wet to extremely dry conditions. If the EDI is less than -2, the indication may be harsh dry conditions, if EDI is between -1.99 and 1.5, then a severe drought is projected, and an EDI between -1.49 and -1 indicates moderate drought. Conditions that are more or less near normal are shown by an EDI between -0.99 and 0.99. In this study, the total monthly rainfall data is being used. The study investigates the fluctuation of rain over thirty-three years using Effective Drought Index (EDI). 4.1 Statistical Description of Data Descriptive statistics are used in the analysis of the data concerning UAE weather and climate change. The statistics are used to describe the basic features of the data. It is especially used in summarizing simple statistics regarding the parameter samples. In this study, together with the graphic analysis, they form the essential basis of every quantitative data analysis. Descriptive statistics in this paper simply describes what is or what the data reveals. The rainfall is basically calculated by adding up all the values of all weather stations of each month. For instance, the total rainfall for the month of January was calculated by summing up the rain values recorded throughout the month from all the weather stations in the UAE. Furthermore, indices for classification of the conditions of UAE with respect to the amount of rainfall recorded at all stations were also analyzed using EDI software to help in the categorization of these thirty-three years as whether they are severe drought, drought, extremely wet or otherwise as well as investigating the correlation between the El Niño/La Niña effect and the intensity of the rainfall over UAE. Thus in summary, this particular study uses mainly quantitative data that are analyzed by SPSS too. The data that was collected are quantitative and ensured the SPSS descriptive traits are applied well. The application covered whole the UAE to ensure that the data collected were representative and homogeneous. The study was done in such a manner that the climatic conditions that affected all parts in the country were analyzed and interpreted. In the analysis, some years receive greater amounts of rainfall, or experience drought conditions, in different seasons. The zonal climatic changes are taken into consideration for purposes of achieving homogeneity and representativeness of data [16]. An instance is the comparison between UAE EDI index (rainfall) and Niño 3.4 index events. The UAE rainfall EDI index and Niño 3.4 index both from 1981 to 2013 were inputted into the SPSS software in order to obtain appropriate statistical descriptive analysis that would lead to understand the correlation of the data samples, as shown in Table 1, and support the output of analysis statistically. As a result, the P-value was less than 0.05 (Sig=0.000). Therefore, there is a strong relationship between UAE rainfall EDI index and Niño 3.4 index. Up to 393 variables were sampled and evaluated in order to determine whether the number of Niño 3.4 indexes were equal to the number of UAE rainfall EDI indexes. The data were analyzed using chi square Goodness of fit test as shown in Table 2. When El Niño and La Niña occur, it takes time to affect other distant parts of the world such as the UAE. The level of significance as shown in Table 2 shows a high significant correlation between the two figures (Niño 3.4 indexes and UAE rainfall EDI indexes). 4.2 Analysis There are 98 weather stations spread throughout the UAE which were used in this study. Rainfall data were collected from these weather stations as shown in Figure 2. The amounts were recorded every day of the month, throughout the year, from Jan 1980 to Sep 2013, in order to detect and study any relation between El Niño and La Niña Years and UAE precipitation. As such, the rainfall data were analysed intensely. Effective Drought Index (EDI) was computed for all weather stations with 33 years of data. Note that the Effective Drought Index (EDI) is a Climatological/Meteorological drought index which measures drought severity and the monthly Niño 3.4 index (which is used to calculate the ONI values) uses at least 30 year periods. These are presented in Table 3. Comparing the computed UAE rainfall EDI with Niño 3.4 values, the drought index calculated from the rainfall data indicated that in Jan 1981, the index was -0.98. After comparing this with the EDI drought indices classification, UAE was classified as near normal. The March 1982 rainfall data produced an index of 2.73. This indicated that in this month of that year, the country was extreme wet. In Dec 1982, the index was 1.59, classifying the country as severe wet. In April, May, and June 1984, the generated indices were -1.04, -1.04, and -1.07 respectively; they were thus classified as moderate drought. In Sept, October, November and December 2009, the indices generated were 2.86, 2.04, 1.36 and 2.51 respectively. These indices show that in the respective months of that year, the UAE was extreme wet, extreme wet, moderate wet and extreme wet. Comparing these indices with the monthly Niño index, that is May and December 1982 and Sept, October, November and December 2009, it has been easily seen that the amount of rainfall increased. This indicated that in those months, the country was considered to be effected by El Niño. Graphing the UAE rainfall EDI and Niño 3.4 indices gives a better picture representing the relationship, as shown in Figure 3. The graph shows the two lines fluctuating in the same path with some differences in the level in some cases but, nevertheless, they all give almost the same rhythms of oscillations. As a matter of fact, for this case, it is better to avoid day to day or month to month comparison. As aforementioned, the occurrence of El Niño or La Niña doesn’t affect UAE precipitation immediately as there is a delay in any effect due to the physical distance involved. This means that atmosphere weather patterns such as the Jet Stream and Rossby Waves and other features take time to reach UAE. The

Mohamed AlEbri *, Hasan Arman, Abdeltawab Shalaby

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General Scientific Researches Vol(4), No (1), March, 2016.

time of the effect varies and depends on the speed of the weather patterns circulation in the atmosphere, however, it was noticed that an impact may happen within three months of the El Niño and La Niña take place. According to the analysis, the periods that are considered wet cover at least 19.95 % of the time, with 72.02 % consider normal and 8.03 % considered drought. These figures are additionally so significant with a p-value less than 0.05, with respect to a significance level of 95% confidence level. Thus, there seems to be a few inconsistencies in the amount of rainfall recorded; most of these figures are statistically significant and could represent the true value in reality. Again, a glance at the drought indices reveal that the drought indices seem to follow a decreasing trend, showing that the UAE keeps on shifting from the normal to arid conditions. An instance is that in January1997, the drought index was 1.19 and in January 2012 the drought index was 0.14. But, nevertheless, this trend is not necessarily set to continue along the same path, as it represented only a short period of climate of the UAE. The future trend could be positive depending on the climate change cycles.

5.

Figures and Tables

Figure 1. The Oceanic Niño Index regions. Showing the region Nino 3.4 (5º North – 5 º South) (170 º – 120 º West) that was used to do the correlation analysis [17]

Figure 2. The distribution of weather stations over UAE. The rainfall data were taken from these 98 stations. GIS software application was used to locate the weather stations

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The Impact of El Nino and La Nina on the United Arab Emirates (UAE) Rainfall General Scientific Researches Vol(4), No (1), March, 2016.

Figure 3. Correlation between UAE EDI and Niño3.4 indices throughout 33 years. UAE Effective Drought Index (EDI) in blue line and Niño3.4 index in red line

Table 1. Statistical correlations of UAE EDI and Niño 3.4 Indices

Rain_EDI Index

Niño 3.4 Index

Pearson Correlation Sig. (2-tailed) N Pearson Correlation Sig. (2-tailed) N

Rain_EDI Index 1 393 .314 .000 393

Niño 3.4 Index .314 .000 393 1 393

Table 2. Chi square test result Value 38.607 393

Linear-by-Linear Association N of Valid Cases

df 1 --

Asymp. Sig. (2-sided) .000 --

Table 3. Definition of the UAE EDI Status Weak El Niño Moderate El Niño Strong El Niño Normal Weak La Niña Moderate La Niña Strong La Niña

6.

Abbr. WEl MEl SEl N- / N+ WLa MLa SLa

Value 0.5 < Niño3.4 < 1.0 1.0 = 1.5 -0.5 < Niño3.4 < 0.5 - 1.0 < Niño3.4